Motif clustering (MC) [1]}. Set of higher-order clustering algorithms by Benson et al. (see sec:rel for details). We consider three versions; (1) MC-Single: Algorithm that gives a single subgraph with near-optimal motif conductance, (2) MC-Rec-Bisection: Recursive bisection algorithm that iteratively finds multiple clusters (starting with the optimal) until the cluster size gets too small (less than 10 nodes) or quality degrades too much (conductance goes above 0.5), (3) MC-k-means: k-means algorithm that is run on the motif adjacency matrix – number of clusters (k) must be specified for this version.

